refine code

comfy/ldm/flux/model.py

@@ -279,15 +279,3 @@ class Flux(nn.Module):
         out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance, control, transformer_options, attn_mask=kwargs.get("attention_mask", None))
         out = out[:, :img_tokens]
         return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:,:,:h_orig,:w_orig]
-
-    def load_state_dict(self, state_dict, strict=True, assign=False):
-        # import pdb; pdb.set_trace()
-        """Override load_state_dict() to add logging"""
-        logging.info(f"Flux load_state_dict start, strict={strict}, state_dict keys count={len(state_dict)}")
-        
-        # Call parent's load_state_dict method
-        result = super().load_state_dict(state_dict, strict=strict, assign=assign)
-        
-        logging.info(f"Flux load_state_dict end, strict={strict}, state_dict keys count={len(state_dict)}")
-        
-        return result

comfy/ldm/modules/diffusionmodules/openaimodel.py

@@ -911,16 +911,4 @@ class UNetModel(nn.Module):
             return self.id_predictor(h)
         else:
             return self.out(h)
-    
+    
-
-    def load_state_dict(self, state_dict, strict=True, assign=False):
-        # import pdb; pdb.set_trace()
-        """Override load_state_dict() to add logging"""
-        logging.info(f"UNetModel load_state_dict start, strict={strict}, state_dict keys count={len(state_dict)}")
-        
-        # Call parent's load_state_dict method
-        result = super().load_state_dict(state_dict, strict=strict, assign=assign)
-        
-        logging.info(f"UNetModel load_state_dict end, strict={strict}, state_dict keys count={len(state_dict)}")
-        
-        return result

comfy/model_base.py

@@ -303,10 +303,7 @@ class BaseModel(torch.nn.Module):
         logging.info(f"model destination device {next(self.diffusion_model.parameters()).device}")
         to_load = self.model_config.process_unet_state_dict(to_load)
         logging.info(f"load model {self.model_config} weights process end")
-        # TODO(sf): to mmap
+        # replace tensor with mmap tensor by assign
-        # diffusion_model is UNetModel
-        # import pdb; pdb.set_trace()
-        # TODO(sf): here needs to avoid load mmap into cpu mem
         m, u = self.diffusion_model.load_state_dict(to_load, strict=False, assign=True)
         free_cpu_memory = get_free_memory(torch.device("cpu"))
         logging.info(f"load model {self.model_config} weights end, free cpu memory size {free_cpu_memory/(1024*1024*1024)} GB")
@@ -389,21 +386,6 @@ class BaseModel(torch.nn.Module):
             area = sum(map(lambda input_shape: input_shape[0] * math.prod(input_shape[2:]), input_shapes))
             return (area * 0.15 * self.memory_usage_factor) * (1024 * 1024)
     
-    def to(self, *args, **kwargs):
-        """Override to() to add custom device management logic"""
-        old_device = self.device if hasattr(self, 'device') else None
-        
-        result = super().to(*args, **kwargs)
-        
-        if len(args) > 0:
-            if isinstance(args[0], (torch.device, str)):
-                new_device = torch.device(args[0]) if isinstance(args[0], str) else args[0]
-        if 'device' in kwargs:
-            new_device = kwargs['device']
-        
-        logging.info(f"BaseModel moved from {old_device} to {new_device}")
-        return result
-
     def extra_conds_shapes(self, **kwargs):
         return {}
 

comfy/model_management.py

@@ -596,7 +596,6 @@ def minimum_inference_memory():
 
 def free_memory(memory_required, device, keep_loaded=[]):
     logging.info("start to free mem")
-    import pdb; pdb.set_trace()
     cleanup_models_gc()
     unloaded_model = []
     can_unload = []

comfy/model_patcher.py

@@ -831,8 +831,11 @@ class ModelPatcher:
             self.backup.clear()
 
                 
-            model_to_mmap(self.model)
+            if device_to is not None:
-            self.model.device = device_to
+                # offload to mmap
+                model_to_mmap(self.model)
+                self.model.device = device_to
+            
             self.model.model_loaded_weight_memory = 0
 
             for m in self.model.modules():
@@ -885,8 +888,7 @@ class ModelPatcher:
                     bias_key = "{}.bias".format(n)
                     if move_weight:
                         cast_weight = self.force_cast_weights
-                        # TODO(sf): to mmap
+                        # offload to mmap
-                        # m is what module?
                         # m.to(device_to)
                         model_to_mmap(m)
                         module_mem += move_weight_functions(m, device_to)